Chinese Part-of-Speech Tagging Method Based on Attention Temporal Network
Because the traditional Chinese part-of-speech tagging(CPOS)models based on statistics and rules hase many problems such as relying heavily on manually designed features,word vectors represent singleness,feature extraction is not comprehensive,this paper proposes an effective Chinese part-of-speech tagging model based on Temporal Convolutional Network with Attention(TCA).This model improved the structure of the original TCN model in three aspects,and proposed the integration of TCA and BiLSTM into CPOS method.In this model,the XLNet model was used to obtain word-level context representation,and TCN's unique causal convolution structure was used to ob-tain higher-level text sequence features and optimize the features through the attention mechanism.Finally,BiLSTM was used to further learn the sequence context features to improve the accuracy of pos tagging.The experimental re-sults show that the performance of this model is significantly improved compared with other models.
Part of Speech taggingTemporal convolutional networkAttention mechanismDeep Learning
NETL
NSTL
万方数据
